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IEEE International Conference on robotics and Automation (ICRA)

作者： Mahdiar Edraki Pauline Maurice Dagmar Sternad Department of Mechanical and Industrial Engineering Northeastern University USA CNRS Inria LORIA Universite de Lorraine Nancy France Department of Biology Electrical and Computer Engineering and Physics Institute for Experiential Robotics Northeastern University USA

An important component for the effective collaboration of humans with robots is the compatibility of their movements, especially when humans physically collaborate with a robot partner. Following previous findings that humans interact more seamlessly with a robot that moves with human-like or biological velocity profiles, this study examined whether humans can adapt to a robot that violates human signatures. The specific focus was on the role of extensive practice and real-time augmented feedback. Six groups of participants physically tracked a robot tracing an ellipse with profiles where velocity scaled with the curvature of the path in biological and non-biological ways, while instructed to minimize the interaction force with the robot. Three of the 6 groups received real-time visual feedback about their force error. Results showed that with 3 daily practice sessions, when given feedback about their force errors, humans could decrease their interaction forces when the robot's trajectory violated human-like velocity patterns. Conversely, when augmented feedback was not provided, there were no improvements despite this extensive practice. The biological profile showed no improvements, even with feedback, indicating that the (non-zero) force had already reached a floor level. These findings highlight the importance of biological robot trajectories and augmented feedback to guide humans to adapt to non-biological movements in physical human-robot interaction. These results have implications on various fields of robotics, such as surgical applications and collaborative robots for industry.

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Decision-Focused Learning for Complex System Identification: HVAC Management System Application

arXiv

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arXiv 2025年

作者： Favaro, Pietro Toubeau, Jean-François Vallée, François Dvorkin, Yury Power Systems and Markets Research Group University of Mons Mons Belgium Ralph O’Connor Sustainable Energy Institute Department of Electrical and Computer Engineering Department of Civil and System Engineering Johns Hopkins University BaltimoreMD United States

As opposed to conventional training methods tailored to minimize a given statistical metric or task-agnostic loss (e.g., mean squared error), Decision-Focused Learning (DFL) trains machine learning models for optimal performance in downstream decision-making tools. We argue that DFL can be leveraged to learn the parameters of system dynamics, expressed as constraint of the convex optimization control policy, while the system control signal is being optimized, thus creating an end-to-end learning framework. This is particularly relevant for systems in which behavior changes once the control policy is applied, hence rendering historical data less applicable. The proposed approach can perform system identification — i.e., determine appropriate parameters for the system analytical model — and control simultaneously to ensure that the model’s accuracy is focused on areas most relevant to control. Furthermore, because black-box systems are non-differentiable, we design a loss function that requires solely to measure the system response. We propose pre-training on historical data and constraint relaxation to stabilize the DFL and deal with potential infeasibilities in learning. We demonstrate the usefulness of the method on a building Heating, Ventilation, and Air Conditioning day-ahead management system for a realistic 15-zone building located in Denver, US. The results show that the conventional RC building model, with the parameters obtained from historical data using supervised learning, underestimates HVAC electrical power consumption. For our case study, the ex-post cost is on average six times higher than the expected one. Meanwhile, the same RC model with parameters obtained via DFL underestimates the ex-post cost only by 3%. © 2025, CC BY-NC-ND.

关键词： Convex optimization

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Opacity Enforcement by Edit Functions Under Incomparable Observations

arXiv

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arXiv 2024年

作者： Duan, Wei Liu, Ruotian Fanti, Maria Pia Hadjicostis, Christoforos N. Li, Zhiwu School of Electro-Mechanical Engineering Xidian University Xi’an710071 China Department of Electrical and Information Engineering Polytechnic University of Bari Bari71025 Italy Department of Electrical and Computer Engineering University of Cyprus Nicosia1678 Cyprus Institute of Systems Engineering Macau University of Science and Technology Taipa999078 China

As an information-flow privacy property, opacity characterizes whether a malicious external observer (referred to as an intruder) is able to infer the secret behavior of a system. This paper addresses the problem of opacity enforcement using edit functions in discrete event systems modeled by partially observed deterministic finite automata. A defender uses the edit function as an interface at the output of a system to manipulate actual observations through insertion, substitution, and deletion operations so that the intruder will be prevented from inferring the secret behavior of the system. Unlike existing work which usually assumes that the observation capabilities of the intruder and the defender are identical, we consider a more general setting where they may observe incomparable subsets of events generated by the system. To characterize whether the defender has the ability to enforce opacity of the system under this setting, the notion of ic-enforceability is introduced. Then, the opacity enforcement problem is transformed to a two-player game, with imperfect information between the system and the defender, which can be used to determine a feasible decision-making strategy for the defender. Within the game scheme, an edit mechanism is constructed to enumerate all feasible edit actions following system behavior. We further show that an icenforcing edit function (if one exists) can be synthesized from the edit mechanism to enforce opacity. Copyright © 2024, The Authors. All rights reserved.

关键词： Finite automata

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Automated Diagnosis of Heart-Lung Diseases in Chest X-ray Images 5

Automated Diagnosis of Heart-Lung Diseases in Chest X-ray Im...

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5th International Conference on engineering Technology and its Applications, IICETA 2022

作者： Alslatie, Mohammad Alquran, Hiam Mustafa, Wan Azani Abu-Qasmieh, Isam Alqudah, Ali Mohammad Alkhayyat, Ahmed The Institute of Biomedical Technology King Hussein Medical Center Royal Jordanian Medical Service Amman Jordan Yarmouk University Biomedical Systems and Medical Informatics Engineering Irbid Jordan Arau Faculty of Electrical Engineering Technology Perlis Malaysia University of Manitoba Biomedical Engineering Program Department of Electrical and Computer Engineering Canada The Islamic University Faculty of Engineering Najaf Iraq

ISBN: (纸本)9781665472159

The state of the art of artificial intelligence (AI) for various medical imaging applications leads to enhanced accuracy, analysis, visualization, and interpretation of chest Xray (CXR) images for diagnosis. Many diseases are diagnosed based on CXR images. In this paper, two types of abnormalities are diagnosed based on AI techniques. The two classes are atelectasis and cardiomegaly. The acquired images are segmented to localize the chest region and then enhanced using gray-level transformation methods. The enhanced images are passed to two pretrained convolutional neural networks (CNNs): shuffle and mobile net. The transfer learning approach is utilized in this stage. The automated features are extracted from the last fully connected layer. Each CNN deserves to have the two most representative features for the two classes. These four features are passed to support the vector machine classifier. The training accuracy reached 100% and the test accuracy was 96.7%. The proposed method can be extended to be a milestone in the classification of all heart-lung diseases that can be diagnosed using chest X-ray images. © 2022 IEEE.

关键词： Convolutional neural networks

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Exploring the Feasibility of Hybrid PV-Wind Energy Plant for Costal-Tourism Sites

Exploring the Feasibility of Hybrid PV-Wind Energy Plant for...

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IEEE Power India Conference

作者： Md Ashraful Islam Md Shahadat Hossenk Fareesa Firdouse Md. Zahid Hasan M M Naushad Ali Golla Suribabu Hayitov Abdulla Nurmatovich Valisher Sapaev Odilbek Uglu Dept of Electrical and Electronic Engineering Green University of Bangladesh Dhaka Bangladesh Department of Electrical and Computer Engineering Gannon University USA Department of Computer Science Engineering Keshav Memorial Institute Of Technology (KMIT) Hyderabad India Civil Engineering Department SRKR Engineering College (A) Bhimavaram A.P. India Department of Transports Systems Urgench State University Uzbekistan Department of General Professional Sciences Mamun University Khiva Uzbekistan

ISBN: (数字)9798350367065

ISBN: (纸本)9798350367072

This study presents the design, optimization, and performance analysis of a PV-Wind hybrid energy system for Kuakata, a coastal region and popular tourist destination in Bangladesh. The hybrid system integrates Vertical Axis Wind Turbines (VAWT) and photovoltaic (PV) technology, addressing both noise reduction and safety concerns, making it particularly suitable for tourist-friendly environments. The system was optimized using HOMER Pro simulation software, which provided the optimal configuration based on technical, economic, and environmental parameters. The results show a 100% renewable energy fraction, a Levelized Cost of Energy (LCOE) of $0.041/kWh, and a Net Present Cost (NPC) of $4.58 million. Additionally, the system demonstrates significant environmental benefits by reducing 230,680 kg of CO 2 emissions annually. This study highlights the feasibility of deploying a PV-Wind hybrid system in coastal regions to meet growing energy demands while promoting sustainability and environmental stewardship. The proposed design also enhances the attraction of Kuakata as a tourist destination by offering a quiet and safe renewable energy solution.

关键词： Photovoltaic systems Renewable energy sources Wind energy Sea measurements Hybrid power systems Software Wind turbines Safety Performance analysis Sustainable development

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Vibrational Stabilization of Cluster Synchronization in Oscillator Networks

arXiv

引用

arXiv 2023年

作者： Qin, Yuzhen Nobili, Alberto Maria Bassett, Danielle S. Pasqualetti, Fabio Department of Mechanical Engineering University of California RiversideCA United States Perceptual Robotics Laboratory at the IIM Institute Department of Excellence in Robotics and A.I. Scuola Superiore Sant’Anna Pisa Italy Department of Bioengineering Department of Electrical & Systems Engineering Department of Physics & Astronomy The Department of Psychiatry Department of Neurology University of Pennsylvania The Santa Fe Institute United States

Cluster synchronization is of paramount importance for the normal functioning of numerous technological and natural systems. Deviations from normal cluster synchronization patterns are closely associated with various malfunctions, such as neurological disorders in the brain. Therefore, it is crucial to restore normal system functions by stabilizing the appropriate cluster synchronization patterns. Most existing studies focus on designing controllers based on state measurements to achieve system stabilization. However, in many real-world scenarios, measuring system states, such as neuronal activity in the brain, poses significant challenges, rendering the stabilization of such systems difficult. To overcome this challenge, in this paper, we employ an open-loop control strategy, vibrational control, which does not requires any state measurements. We establish some sufficient conditions under which vibrational inputs stabilize cluster synchronization. Further, we provide a tractable approach to design vibrational control. Finally, numerical experiments are conducted to demonstrate our theoretical findings. Copyright © 2023, The Authors. All rights reserved.

关键词： Brain

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A Parallel and Asynchronous Blob Shape Descriptor Implemented in CMOS Technology

A Parallel and Asynchronous Blob Shape Descriptor Implemente...

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International Conference on Microelectronics, MIEL

作者： P. Bogacki M. Długosz T. Talaśka R. Długosz Aptiv Services Poland Kraków Poland Institute of Telecommunications Faculty of Computer Science Electronics and Telecommunications AGH University of Science and Technology Kraków Poland Faculty of Control Robotics and Electrical Engineering Institute of Automation and Robotics Division of Signal Processing and Electronic Systems Poznan University of Technology Poznan Poland Faculty of Telecommunication Computer Science and Electrical Engineering Bydgoszcz University of Science and Technology Bydgoszcz Poland

The paper presents a family of novel light blob shape descriptors for use in selected active safety algorithms used in Advanced Driver Assistance systems (ADAS). One of the motivations was to obtain a descriptor that would ensure low computational complexity. This makes it easy to implement both in software and hardware. One assumption is that the location of the center of a given light spot is approximately known. The principle of its operation is then to count white pixels in selected directions, starting from this central point. A key issue here is an efficient way of determining indexes of particular pixels belonging to the image patch, as well as the location of points representing places where the white area turns into black. In the case of a hardware implementation, this can be done using a parallel circuit operating in asynchronous mode, without the need for a control clock.

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Distributed Learning by Local Training ADMM

Distributed Learning by Local Training ADMM

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IEEE Conference on Decision and Control

作者： Xiaoxing Ren Nicola Bastianello Karl H. Johansson Thomas Parisini Department of Electrical and Electronic Engineering Imperial College London London United Kingdom 2School of Electrical Engineering and Computer Science and Digital Futures KTH Royal Institute of Technology Stockholm Sweden Department of Electronic Systems Aalborg University Denmark Department of Engineering and Architecture University of Trieste Trieste Italy

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, we focus on distributed learning over peer-to-peer networks. In particular, we address the challenge of expensive communications (which arise when e.g. training neural networks), by proposing a novel local training algorithm, LTADMM. We extend the distributed ADMM enabling the agents to perform multiple local gradient steps per communication round (local training). We present a preliminary convergence analysis of the algorithm under a graph regularity assumption, and show how the use of local training does not compromise the accuracy of the learned model. We compare the algorithm with the state of the art for a classification task, and in different set-ups. The results are very promising showing a great performance of LT-ADMM, and paving the way for future important theoretical developments.

关键词： Training computer aided instruction Analytical models Accuracy Distance learning Neural networks Classification algorithms Topology Peer-to-peer computing Convergence

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SubPipe: A Submarine Pipeline Inspection Dataset for Segmentation and Visual-inertial Localization

SubPipe: A Submarine Pipeline Inspection Dataset for Segment...

引用

OCEANS

作者： Olaya Álvarez-Tuñón Luiza Ribeiro Marnet Martin Aubard László Antal Maria Costa Yury Brodskiy Department of Electrical and Computer Engineering Artificial Intelligence in Robotics Laboratory (AiR-Lab) Aarhus University Aarhus C Denmark EIVA a/s Skanderborg Denmark OceanScan Marine Systems & Technology Matosinhos Portugal RWTH Aachen University Aachen Germany

ISBN: (数字)9798350362077

ISBN: (纸本)9798350362084

This paper presents SubPipe, an underwater dataset for SLAM, object detection, and image segmentation. SubPipe has been recorded using a lightweight autonomous underwater vehicle (LAUV), operated by OceanScan MST, and carrying a sensor suite including two cameras, a side-scan sonar, and an inertial navigation system, among other sensors. The AUV has been deployed in a pipeline inspection environment with a submarine pipe partially covered by sand. The AUV's pose ground truth is estimated from the navigation sensors. The side-scan sonar and RGB images include object detection and segmentation annotations, respectively. State-of-the-art segmentation, object detection, and SLAM methods are benchmarked on SubPipe to demonstrate the dataset's challenges and opportunities for leveraging computer vision algorithms. To the authors' knowledge, this is the first anno-tated underwater dataset providing a real pipeline inspection scenario. The dataset and experiments are publicly avail-able online at https://***/remaro-network/SubPipe-dataset.

关键词： Location awareness Image segmentation Simultaneous localization and mapping Oceans Pipelines Object detection Inertial navigation

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Effects of Robotic Expertise and Task Knowledge on Physical Ergonomics and Joint Efficiency in a Human-Robot Collaboration Task

Effects of Robotic Expertise and Task Knowledge on Physical ...

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IEEE-RAS International Conference on Humanoid Robots

作者： Matteo Pantano Arianna Curioni Daniel Regulin Tobias Kamps Dongheui Lee Technology department Siemens Aktiengesellschaft Munich Germany Department of Electrical and Computer Engineering Human-Centered Assistive Robotics Technical University of Munich Munich Germany Autonomous Systems Institut für Computertechnik Technische Uni-versitat Wien Vienna Austria Institute of Robotics and Mechatronics German Aerospace Center Wessling Germany

With the trend of low batch manufacturing, more and more small and medium enterprises are leaning towards adopting collaborative robots to increase productivity and improve operator well-being. However, robots are often programmed by robot experts rather than the operators effectively working with the machine. Therefore, operators may perceive low levels of task autonomy due to the unpredictability of robot motions. Empowering operators to make their own choices regarding robot motions can improve such feelings. However, research in cognitive science shows that allowing operators to decide on robot motions in a collaborative task could be influenced by how people consider their travel path and their partner's action. To better understand these relations, considering preliminary results from a previous study, we designed a user study where we tested operators' decisions in a collaborative task where groups of robot experts and novices were asked to choose their preferred task configuration among four possible options that differed in terms of operators' physical ergonomics and robot travel path. Our results show that robotic experts prioritize joint team work rather than their ergonomics. Contrarily, novices prioritize individual efforts and tend to reduce the robot travel path while keeping their travel path constant, maintaining good physical ergonomics. In conclusion, providing operators with task decision autonomy can be advantageous, but operator background must be considered to ensure optimal physical ergonomics and travel paths.

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